This study aims to investigate the qualitative and quantitative image quality of low-dose high-resolution (LD-HR) lung CT scans acquired with the first clinical approved photon counting CT (PCCT) scanner. Furthermore, the radiation dose used by the PCCT is compared to a conventional CT scanner with an energy-integrating detector system (EID-CT). Twenty-nine patients who underwent a LD-HR chest CT scan with dual-source PCCT and had previously undergone a LD-HR chest CT with a standard EID-CT scanner were retrospectively included in this study. Images of the whole lung as well as enlarged image sections displaying a specific finding (lesion) were evaluated in terms of overall image quality, image sharpness and image noise by three senior radiologists using a 5-point Likert scale. The PCCT images were reconstructed with and without a quantum iterative reconstruction algorithm (PCCT QIR+/−). Noise and signal-to-noise (SNR) were measured and the effective radiation dose was calculated. Overall, image quality and image sharpness were rated best in PCCT (QIR+) images. A significant difference was seen particularly in image sections of PCCT (QIR+) images compared to EID-CT images (p < 0.005). Image noise of PCCT (QIR+) images was significantly lower compared to EID-CT images in image sections (p = 0.005). In contrast, noise was lowest on EID-CT images (p < 0.001). The PCCT used significantly less radiation dose compared to the EID-CT (p < 0.001). In conclusion, LD-HR PCCT scans of the lung provide better image quality while using significantly less radiation dose compared to EID-CT scans.

BACKGROUND AND PURPOSE: Detailed insight into the composition of thrombi retrieved from patients with ischemic stroke by mechanical thrombectomy might improve pathophysiologic understanding and therapy. Thus, this study searched for links between histologic thrombus composition and stroke subtypes and mechanical thrombectomy results. MATERIALS AND METHODS: Thrombi from 85 patients who had undergone mechanical thrombectomy for acute ischemic stroke between December 2016 and March 2018 were studied retrospectively. Thrombi were examined histologically. Preinterventional imaging features, stroke subtypes, and interventional parameters were re-analyzed. Statistical analysis was performed with the Kruskal-Wallis test, Mann-Whitney U test, or Spearman correlation as appropriate. RESULTS: Cardioembolic thrombi had a higher percentage of macrophages and a tendency toward more platelets than thrombi of large-artery atherosclerotic stenosis (P = .021 and .003) or the embolic stroke of undetermined source (P = .037 and .099) subtype. Thrombi prone to fragmentation required the combined use of contact aspiration and stent retrieval (P = .021) and were associated with an increased number of retrieving maneuvers (P = .001), longer procedural times (P = .001), and a higher lymphocyte content (P = .035). CONCLUSIONS: We interpreted the higher macrophage and platelet content in cardioembolic thrombi compared with large-artery atherosclerotic stenosis or embolic stroke of undetermined source thrombi as an indication that the latter type might be derived from an atherosclerotic plaque rather than from an undetermined cardiac source. The extent of thrombus fragmentation was associated with a more challenging mechanical thrombectomy and a higher lymphocyte content of the thrombi. Thus, thrombus fragmentation not only might be caused by the recanalization procedure but also might be a feature of a lymphocyte-rich, difficult-to-retrieve subgroup of thrombi.

Objectives: Low-field MRI at 0.55 Tesla (T) with deep learning image reconstruction has recently become commercially available. The objective of this study was to evaluate the image quality and diagnostic reliability of knee MRI performed at 0.55T compared with 1.5T. Methods: A total of 20 volunteers (9 female, 11 male; mean age = 42 years) underwent knee MRI on a 0.55T system (MAGNETOM Free.Max, Siemens Healthcare, Erlangen, Germany; 12-channel Contour M Coil) and a 1.5T scanner (MAGNETOM Sola, Siemens Healthcare, Erlangen, Germany; 18-channel transmit/receive knee coil). Standard two-dimensional (2D) turbo spin echo (TSE), fat-suppressed (fs) proton density-weighted (PDw), T1w TSE, and T2w TSE sequences were acquired in approximately 15 min. In total, 2 radiologists blinded to the field strength subjectively assessed all MRI sequences (overall image quality, image noise, and diagnostic quality) using a 5-point Likert scale (1–5; 5 = best). Additionally, both radiologists evaluated the possible pathologies of menisci, ligaments, and cartilage. Contrast ratios (CRs) of different tissues (bone, cartilage, and menisci) were determined on coronal PDw fs TSE images. The statistical analysis included Cohen’s kappa and the Wilcoxon rank sum test. Results: The overall image quality of the 0.55T T2w, T1w, and PDw fs TSE sequences was diagnostic and rated similar for T1w (p > 0.05), but lower for PDw fs TSE and T2w TSE compared with 1.5T (p < 0.05). The diagnostic accordance of meniscal and cartilage pathologies at 0.55T was similar to 1.5T. The CRs of the tissues were not significantly different between 1.5T and 0.55T (p > 0.05). The inter-observer agreement of the subjective image quality was generally fair between both readers and almost perfect for the pathologies. Conclusions: Deep learning-reconstructed TSE imaging at 0.55T yielded diagnostic image quality for knee MRI compared with standard 1.5T MRI. The diagnostic performance of meniscal and cartilage pathologies was equal for 0.55T and 1.5T without a significant loss of diagnostic information.

Limbic encephalitis (LE) is an immune-related, sometimes paraneoplastic process of the central nervous system. Initial diagnosis and treatment are based on the clinical presentation as well as antibody profiles and MRI. This study investigated the diagnostic value of integrated ¹⁸F-FDG-PET/MRI in the diagnostic work-up of patients with LE for a cerebral and whole-body imaging concept. Twenty patients with suspected LE were enrolled in this prospective study. All patients underwent a dedicated PET/MRI protocol of the brain as well as the whole-body. Two neuroradiologists, one body radiologist and one nuclear medicine physician performed blinded consensus readings of each corresponding MRI and PET/MRI dataset of the brain and whole-body. Diagnostic confidence was evaluated on a Likert scale. Based on integrated PET/MRI 19 / 20 patients were found to show morphologic and / or metabolic changes indicative of LE, whereas sole MRI enabled correct identification in 16 / 20 patients. Three patients with negative MRI showed metabolic changes of the limbic system or extra-limbic regions, shifting the diagnosis from (negative) MRI to positive for LE in PET/MRI. Whole-body staging revealed suspected lesions in 2/20 patients, identified by MRI and PET, one confirmed as malignant and one false positive. Diagnostic confidence for cerebral and whole-body imaging reached higher scores for PET/MRI (cerebral: 2.7 and whole body: 4.8) compared to MRI alone (cerebral: 2.4 and whole body: 4.5). LE diagnosis remains challenging for imaging as it shows only subtle imaging findings in most patients. Nevertheless, based on the simultaneous and combined analysis of morphologic and metabolic data, integrated PET/MRI may enable a dual platform for improved diagnostic confidence and overall detection of LE as well as whole-body imaging for exclusion of paraneoplastic LE.

Detection and diagnosis of early and subclinical stages of Alzheimer’s Disease (AD) play an essential role in the implementation of intervention and prevention strategies. Neuroimaging techniques predominantly provide insight into anatomic structure changes associated with AD. Deep learning methods have been extensively applied towards creating and evaluating models capable of differentiating between cognitively unimpaired, patients with Mild Cognitive Impairment (MCI) and AD dementia. Several published approaches apply information fusion techniques, providing ways of combining several input sources in the medical domain, which contributes to knowledge of broader and enriched quality. The aim of this paper is to fuse sociodemographic data such as age, marital status, education and gender, and genetic data (presence of an apolipoprotein E (APOE)-ε4 allele) with Magnetic Resonance Imaging (MRI) scans. This enables enriched multi-modal features, that adequately represent the MRI scan visually and is adopted for creating and modeling classification systems capable of detecting amnestic MCI (aMCI). To fully utilize the potential of deep convolutional neural networks, two extra color layers denoting contrast intensified and blurred image adaptations are virtually augmented to each MRI scan, completing the Red-Green-Blue (RGB) color channels. Deep convolutional activation features (DeCAF) are extracted from the average pooling layer of the deep learning system Inception_v3. These features from the fused MRI scans are used as visual representation for the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) classification model. The proposed approach is evaluated on a sub-study containing 120 participants (aMCI = 61 and cognitively unimpaired = 59) of the Heinz Nixdorf Recall (HNR) Study with a baseline model accuracy of 76%. Further evaluation was conducted on the ADNI Phase 1 dataset with 624 participants (aMCI = 397 and cognitively unimpaired = 227) with a baseline model accuracy of 66.27%. Experimental results show that the proposed approach achieves 90% accuracy and 0.90 F₁-Score at classification of aMCI vs. cognitively unimpaired participants on the HNR Study dataset, and 77% accuracy and 0.83 F₁-Score on the ADNI dataset.

Background: The purpose of the present study was the evaluation of the image quality of polyenergetic and monoenergetic reconstructions (PERs and MERs) of CT angiographies (CTAs) of the head and neck acquired with the novel photon counting CT (PCCT) method in clinical routine. Methods: Thirty-seven patients were enrolled in this retrospective study. Quantitative image parameters of the extracranial, intracranial and cerebral arteries were evaluated for the PER and MER (40–120 keV). Additionally, two radiologists rated the perceived image quality. Results: The mean CTDI_vol used in the PCCT was 8.31 ± 1.19 mGy. The highest signal within the vessels was detected in the 40 keV MER, whereas the lowest noise was detected in the 115 keV MER. The most favorable contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR) were detected in the PER and low keV MER. In the qualitative image analysis, the PER was superior to the MER in all rated criteria. For MER, 60–65 keV was rated as best image quality. Conclusion: Overall, PCCT offers excellent image quality for CTAs of the head and neck. At the current state, the PER of the PCCT seems to be the most favorable reconstruction for diagnostic reporting.